14 25

Cited 0 times in

Cited 0 times in

The Asian Pacific Association of the Study of the Liver expert survey on artificial intelligence-assisted reporting of liver histopathology in metabolic dysfunction associated fatty liver disease

Authors
 Elangovan, H.  ;  Akbary, K.  ;  Rastogi, A.  ;  Wee, A.  ;  Soon, G.  ;  Adams, L.  ;  Carr-Boyd, E.  ;  Clouston, A.  ;  Cooper, C. L.  ;  Chan, W. K.  ;  Dan, Y. Y.  ;  Dela-Cruz, R.  ;  Goh, G.  ;  Hamid, S. S.  ;  Huang, D. Q.  ;  Kawaguchi, T.  ;  Kim, W.  ;  Kim, S. U.  ;  Jia, J. D.  ;  Liu, C. J.  ;  Liu, F.  ;  Leow, W. Q.  ;  Muthiah, M. D.  ;  Ng, I.  ;  Payawal, D.  ;  Pan, A. F.  ;  Pervez, S.  ;  Shiha, G.  ;  Tanwandee, T.  ;  Tanaka, Y.  ;  Thiyaphat, L.  ;  Vij, M.  ;  Yilmaz, Y.  ;  Yilmaz, F.  ;  Yu, M. L.  ;  Zalata, K.  ;  Zheng, M. H.  ;  Fan, J. G.  ;  Sarin, S. K.  ;  Wong, V.  ;  George, J. 
Citation
 HEPATOLOGY INTERNATIONAL, 2026-05 
Journal Title
HEPATOLOGY INTERNATIONAL
ISSN
 1936-0533 
Issue Date
2026-05
Keywords
MAFLD ; MASH ; Artificial Intelligence ; Histopathology
Abstract
Introduction Artificial intelligence (AI) and digital pathology have the potential to augment liver biopsy interpretation in MAFLD in clinical practice and trials assessment. However, attitudes and barriers to its implementation have not been systematically explored. Methods A survey focusing on conventional liver histology, digital pathology and its AI applications in MAFLD/MASH was conducted among hepatologists and liver pathologists in the Asia Pacific region. Results AI-assisted digital pathology is perceived to be a valuable addition to existing histological reporting in MAFLD/ MASH. Defined standards for application and validation of AI models are important priorities for their implementation. Conclusion There is consensus among clinical experts in the Asia Pacific that AI-assisted histological assessment is useful in MAFLD/MASH interpretation. However, there remain important challenges to the adoption of these technologies into routine clinical workflows.
Files in This Item:
93151.pdf Download
DOI
10.1007/s12072-026-11092-6
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Seung Up(김승업) ORCID logo https://orcid.org/0000-0002-9658-8050
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/212652
사서에게 알리기
  feedback

qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse

Links